Fuzzy Linguistic Model Research Articles

SICTQUAL: A fuzzy linguistic multi-criteria model to assess the quality of service in the ICT sector from the user perspective

The Information and Communication Technologies (ICTs) play an important role in the economic development, making it necessary to assess the quality of service perceived by consumers in this sector. The most effective quality assessment from the consumer perspective is still to be researched, yet the most common approach is oriented towards quantitative indicators. This study proposes to use a two-dimensional model that combines the widely accepted segmentation of ICTs with elements from the SERVQUAL quality model. This model, useful in multi-criteria decision-making situations, has been developed using the 2-tuple linguistic representation and fuzzy logic principles. This methodology prevents data loss during processing and provides relevant information through 16 indicators related to the quality of service. Besides, an expert-based mechanism is defined for the use of historical information extracted from completed surveys. As a practical case, this mechanism is applied to the historical information of a telecommunications company for assessing the quality of the service provided to its customers.

Fuzzy linguistic prediction model for sinoatrial node field potential analysis in acute hyperglycemia environment.

The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.

Some interesting properties of the fuzzy linguistic model based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information

The management of hesitant fuzzy information is a topic of special interest in fuzzy decision making. In this paper, we focus on the use and properties of the fuzzy linguistic modelling based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information. Among these properties, we can highlight the existence of aggregation functions with no need of transformations or the possibility of a greater flexibilization of the opinions of the experts, even using different linguistic chains (multigranularity). Furthermore, based on these properties we perform a comparison between this model and the one based on hesitant fuzzy linguistic term sets, showing the advantages of the former with respect to the latter. Finally, a fuzzy decision making model based on discrete fuzzy numbers is proposed.

Fuzzy model approach for estimating time of hospitalization due to cardiovascular diseases.

A fuzzy linguistic model based on the Mamdani method with input variables, particulate matter, sulfur dioxide, temperature and wind obtained from CETESB with two membership functions each was built to predict the average hospitalization time due to cardiovascular diseases related to exposure to air pollutants in São José dos Campos in the State of São Paulo in 2009. The output variable is the average length of hospitalization obtained from DATASUS with six membership functions. The average time given by the model was compared to actual data using lags of 0 to 4 days. This model was built using the Matlab v. 7.5 fuzzy toolbox. Its accuracy was assessed with the ROC curve. Hospitalizations with a mean time of 7.9 days (SD = 4.9) were recorded in 1119 cases. The data provided revealed a significant correlation with the actual data according to the lags of 0 to 4 days. The pollutant that showed the greatest accuracy was sulfur dioxide. This model can be used as the basis of a specialized system to assist the city health authority in assessing the risk of hospitalizations due to air pollutants.

Fuzzy linguistic in geometric modeling

Fuzzy Linguistic is an extension of fuzzy set theory was introduced by Zadeh. Normally fuzzy linguistic is often associated with linguistic variables generated by a function modifier is also known as a hedges. This paper discusses the theorems and definitions of fuzzy linguistic perspective geometric modeling to produce Fuzzy Linguistic Control Point (FLCP). Fuzzy Linguistic Control Points have been blended in with the spline basic functions of the model to produce a few splines’s model are characterized by fuzzy linguistics. At the end of this article will discuss some numerical examples of Fuzzy Linguistic Bezier Model.

Impact of malicious servers over trust and reputation models in wireless sensor networks

This article deals with the impact of malicious servers over different trust and reputation models in wireless sensor networks. First, we analysed the five trust and reputation models, namely BTRM-WSN, Eigen trust, peer trust, power trust, linguistic fuzzy trust model. Further, we proposed wireless sensor network design for optimisation of these models. Finally, influence of malicious servers on the behaviour of above mentioned trust and reputation models is discussed. Statistical analysis has been carried out to prove the validity of our proposal.

Evolutionary Prisoner's Dilemma in updating fuzzy linguistic model to damp power system oscillations

This study presents a novel technique to extract and update the rules of fuzzy linguistic model (FLM). The interrelations between inputs and outputs of a FLM are assigned using payoff matrix through Prisoner's Dilemma (PD). These interrelations are updated by updating the payoff matrix through Evolutionary Algorithm based PD to propose Evolutionary Algorithm based fuzzy Prisoner's Dilemma linguistic model (FPDLM). The effect of evolutionary algorithm based FPDLM as power system stabiliser is tested on single machine infinite bus system and also multi machine system.

A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling

Recommender systems evaluate and filter the vast amount of information available on the Web, so they can be used to assist users in the process of accessing to relevant information. In the literature we can find countless approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. In this sense, building accurate profiles plays an essential role in this context making the system’s success depend to a large extent on the ability of the learned profiles to represent the user’s preferences and needs. An ontology works very well to characterize the users profiles involved in the process of generating recommendations. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modeling, so that in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.

A proportional 3-tuple fuzzy linguistic representation model for screening new product projects

It is critically important for companies to screen new product projects before they are launched to the market. So far, many approaches have been developed for tackling the process of screening product innovations. Due to uncertain, vague and incomplete information as well as dynamically complex process regarding to new product development (NPD), a fuzzy linguistic approach employed linguistic assessments and the fuzzy-set-based computation is reasonable for screening new products. However, such a fuzzy linguistic approach faces with various defects and limitations, such as loss of information, failing in considering the aspects related to human nature on uncertain subjective judgments etc. These defects and limitations lead to a dilemma, i.e., it’s very difficult to screen new product projects reasonably and precisely. In this paper, we propose a notion of proportional 3-tuple to represent a linguistic assessment and related ignoring information, and a preference-preserving proportional 3-tuple transformation for the unification of linguistic assessments represented by proportional 3-tuples between two different linguistic term sets. On this basis, a proportional 3-tuple fuzzy linguistic representation model for screening new product projects is developed. It is shown that the proposed model is flexible to handle uncertain, vague and incomplete information related to screening new product projects. It not only allows evaluators to express their subjective judgments with different confidence levels, but is also able to deal with incomplete linguistic assessments. Ultimately, the proposed model also improves the precision and reasonability of the screening result.

An Aggregated Fuzzy Naive Bayes Data Classifier

In this study, an Aggregated Fuzzy Naive Bayes Classifier is proposed for decision-making problems where both linguistic and numerical information are available. In the solution process of such problems, all attributes are considered as fuzzy numbers and a procedure based on 2-tuple fuzzy linguistic representation model is generated for combining them. This procedure and subsequent Fuzzy Naive Bayes classification are performed based on arithmetic operations defined by Chen’s function principle. The proposed method was demonstrated on 2 well-known examples from the literature in which both numerical and linguistic attributes were considered. The results show that the proposed Aggregated Fuzzy Naïve Bayes Classifier is notably efficient in decision-making where the attributes are in more realistic forms.

A 2-tuple Fuzzy Linguistic RFM Model and Its Implementation

Abstract RFM is a model used to analyze the behavior of customer by means of three variables: Recency, Frequency and Monetary. The scores of these variables are expressed by an integer number, typically, in the range 1..5. The fuzzy linguistic approach is a tool intended for modeling qualitative information in a problem. In this paper, we propose to manage these RFM scores using the 2–tuple model which is a fuzzy linguistic model of information representation that carries out processes of “computing with words” without the loss of information. The proposed model permits us an easy linguistic interpretability and let us obtain a more precise representation of the RFM scores. Therefore, by interpreting these linguistic scores, decision makers can effectively identify valuable customers and consequently develop more effective marketing strategy. Additionally, we present an IBM SPSS Modeler implementation of this model. As a particular case study, we show an application example in order to select the customer of a loyalty campaign.

Integrating Ontologies and Fuzzy Logic to Represent User-Trustworthiness in Recommender Systems

Abstract Recommender systems can be used to assist users in the process of accessing to relevant information. In the literature we can find sundry approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. Building accurate profiles plays an essential role in this context, so that the system's success depend to a large extent on the ability of the learned profiles to represent the user's preferences. An ontology works very well to characterize the users profiles. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modelling, this way in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.

On Multi-granular Fuzzy Linguistic Modelling in Decision Making

Abstract Nowadays, the human-computer interaction is being a hot topic. In such a way, several methods have been proposed to deal with multi-granularity when people with different knowledge level express their preferences on the same concept using linguistic notation, that is, words instead of numbers. This is a critical problem in group decision making scenarios, but all the existing approaches have their own advantages and drawbacks. Therefore, some work better in certain environments than others. In such a way, choosing the best method in each situation is critical for obtaining good quality results. In this contribution, an analysis on the different fuzzy linguistic multi-granular modelling approaches is presented in order to provide the reader some advice of what method should be chosen depending on the problem and the quality of results that the user expects to obtain.

A Novel AHP-Based Benefit Evaluation Model of Military Simulation Training Systems

With the constantly changing patterns of war, more technologically exquisite weapons are designed, increasing in cost and complexity. Training maneuvers with live ammunition are expensive and are prone to accidental casualties. Thus, many countries are gradually adopting simulation training systems to replace some actual exercises to reduce casualties and still maintain maximum combat readiness. However, each simulation training system has a different background with regard to time, source, function goal, and quality of environment and staff. It is also more difficult to assess the benefit of simulation training systems. Moreover, traditional benefit assessments of military simulation training systems have merely considered their efficiency, not safety, causing biased conclusions. To solve these issues, this paper integrates the analytic hierarchy process (AHP), important-performance analysis (IPA), and the 2-tuple fuzzy linguistic representation model to determine the benefits of simulation training systems. To verify the proposed approach, a numerical example of the evaluation of a training simulator system’s benefit is adopted. Compared with the traditional AHP method, the proposed method does not lose any valuable information that is provided by experts and also considers training safety. Further, these data are presented in 2-dimensional graphs for managers to further guide the decision-making process.

Stochastic partner selection for virtual enterprises: a chance-constrained approach

A virtual enterprise (VE) is a temporary organisation that pools the core competencies of its member enterprises in order to exploit fast-changing market opportunities. Making successful collaborative partnerships is, in this context, a major challenge in today’s competitive business environments. The success of such a ‘virtual’ organisation is strongly dependent on its composition, and the selection of partners becomes therefore a crucial issue. This problem is particularly difficult because of the uncertainties related to information, market dynamics, customer expectations and technology speed-up, with a strongly stochastic decision-making context. In this paper, a chance-constrained approach to rank alternative VE configurations in business environments with uncertainty, and vague and random information, is proposed. This approach is based on a two-stage model: a chance-constraint multi-objective directional Tabu Search metaheuristic, complemented by a 2-tuple fuzzy linguistic representation model. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model

Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today’s conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions.

Toward SLA-constrained service composition: An approach based on a fuzzy linguistic preference model and an evolutionary algorithm

In a market-oriented service computing environment, both back-end SLA (service level agreement) offers and front-end SLA requirements should be considered when performing service composition. In this paper, we address the optimization problem of SLA-constrained service composition and focus on the following issues: the difficulties related to preference definition and to weight assignment, the limitation of linear utility functions in identifying preferred skyline solutions, and the efficiency and scalability requirements of the optimization algorithm. We present a systematic approach based on a fuzzy preference model and on evolutionary algorithms. Specifically, we first model this multi-objective optimization problem using the weighted Tchebycheff distance rather than a linear utility function. We then present a fuzzy preference model for preference representation and weight assignment. In the model, a set of fuzzy linguistic preference terms and their properties are introduced for establishing consistent preference order of multiple QoS dimensions, and a weighting procedure is proposed to transform the preference into numeric weights. Finally, we present two evolutionary algorithms, i.e., single_EA and hybrid_EA, that implement different optimization objectives and that can be used in different SLA management scenarios for service composition. We conduct a set of experimental studies to evaluate the effectiveness of the proposed algorithms in determining the optimal solutions, and to evaluate their efficiency and scalability for different problem scales.

On multi-granular fuzzy linguistic modeling in group decision making problems: A systematic review and future trends

The multi-granular fuzzy linguistic modeling allows the use of several linguistic term sets in fuzzy linguistic modeling. This is quite useful when the problem involves several people with different knowledge levels since they could describe each item with different precision and they could need more than one linguistic term set. Multi-granular fuzzy linguistic modeling has been frequently used in group decision making field due to its capability of allowing each expert to express his/her preferences using his/her own linguistic term set. The aim of this research is to provide insights about the evolution of multi-granular fuzzy linguistic modeling approaches during the last years and discuss their drawbacks and advantages. A systematic literature review is proposed to achieve this goal. Additionally, some possible approaches that could improve the current multi-granular linguistic methodologies are presented.

Modified Proportional 2-Tuple and its Application in Uncertainty Environment

A modified proportional 2-tuple was proposed to apply to the term sets in that the distance of the adjacent tems is not equal,and the comparison methods as well as the aggregation operators were proposed too.And the modified proportional 2-tuple fuzzy linguistic representation model is extended to uncertainty modified proportional 2-tuple fuzzy linguistic representation model,and the new comparison rule and aggregation operators were provided.

Fuzzy approach - a new chapter in the methodology of psychology?

Abstract This paper aims to briefly introduce the main idea behind the fuzzy approach and to identify the areas and problems encountered in the humanities that might profit from using this approach. Based on a short overview of selected applications of fuzzy in psychology we identify key areas in which the fuzzy approach has already been applied, and propose a list of general types of problems that the fuzzy approach may provide solutions for in psychology and the humanities in general. These types of problems are illustrated using practical examples. The benefits and possible shortcomings of using the fuzzy approach compared to classical approaches in use today are discussed. The goal of this paper is to indicate areas in research and practice in the humanities, where modern mathematical tools—in this case linguistic fuzzy modeling—have already been used or might prove promising.